The ultimate Data Scientist tool
to work efficiently with Big Data
in Hadoop ecosystem
For Data Analysis
No more analytics vs. development.
Your Data Scientists can do computing on a Hadoop cluster on their local machine – fast end easy.
The familiar Python and Jupiter Notebook for data analysis and model designing.
Python models automatically transform into industrial code
running on a cluster.
Launching production analytic models without Data Analytics
Loss of data
Data Scientists cannot directly access data on a Hadoop cluster – they don’t understand how data is stored,
and they don’t know about the types of data available
Loss of accuracy
To put an analytical model in commercial operation, you need to convert it from Python to Java/Scala. This affects the accuracy of the algorithms. The actual KPIs of a production model on a Hadoop cluster will be significantly lower than the KPIs of algorithms created in Python on a local machine.
Many iterations to adjust the required precision of the industrial model.
Tensions between data scientists and data engineers as they use different technology stacks.
Data Analytics Makes Work With Industrial Models Easy!
Import and Export
Analytics and Modeling
Squeeze the maximum out of data – any data
Manage the inbound data streams of your cluster
Aggregate data from various sources
Customize the import schedule in a few clicks
Easy to integrate with existing systems in the company
Export model results to
any external applications
Work with data in the familiar Jupiter Notebook interface
right on the Hadoop cluster
Create your own unique analytical models or
use templates and manage computing on the cluster
Put analytic models in production
in one click, directly from the analytics dashboard
The full picture of the data analysis before your eyes
Control the quality of machine learning
in real time
Track data availability 24/7 and respond quickly
Visualization of the relations between the calculations gives you the full picture
of your data use
Make decisions based on data
Learn about the most valuable data and control
the most important model parameters
Timely reports help you improve
the overall performance of your team
Create, save and edit projects. Separate sample data for machine learning and model control.
Use SQL queries to calculate parameters. Put models in production directly from the analytics UI.
Support SQL queries to import data
from databases into Hadoop.
Manage DBMS connections.
Start or restart import and processing.
Preview the import result to avoid errors.
Data Export UI
Export data to external databases or files
Use data encryption
Preview the export result to avoid errors
Visual history of launches
Notification of errors in model operation
Tracking related calculations
Dashboards with current metric values
Color indication of metric deviations
Interactive retrospective graphics for each metric
Implement big data analytics painlessly
Integrate with any analytical systems
Improve awareness about the work of your employees
Optimize your resources and focus your efforts
Increase the number of working models
Reduce model development time
Increase the speed and accuracy
Be aware about the number of working, broken models and their schedule
Actively use reports and receive notifications
Reduce Time-to-Market for new features and parameters of customer analytics
Turn insights into actions fast
Boost sales and profits by introducing new analytic models
Be the first to respond to the challenges of a changing market
One user-friendly toolkit for all tasks
Fast model implementation
Easy-to-use modeling environment
Access to all data from different sources
Clear results and high precision without over-engagement of Data Engineers and Developers
Easy to deploy Big Data and
Machine Learning into business processes
in 1-2 weeks
Instead of 1.5-2 months
of staff 1 = 3
A Data Scientist is enough to analyze big data
Training a Data Scientist is easier than learning the entire Hadoop ecosystem. The basic course lasts 20 hours.
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